Estimation of spatial high resolution temperature fields for the Tibetan Plateau and the adjacent lowlands based on an elevation and bias corrected ERA-Interim Data Set

Abstract HKT-ISTP 2013 B

Variability of the cold season climate in central asia. Part II: Hydroclimatic predictability

International audienceCentral Asia (CA) is subjected to a large variability of precipitation. This study presents a statistical model, relating precipitation anomalies in three subregions of CA in the cold season (November-March) with various predictors in the preceding October. Promising forecast skill is achieved for two subregions covering 1) Uzbekistan, Turkmenistan, Kyrgyzstan, Tajikistan, and southern Kazakhstan and 2) Iran, Afghanistan, and Pakistan. ENSO in October is identified as the major predictor. Eurasian snow cover and the quasi-biennial oscillation further improve the forecast performance. To understand the physical mechanisms, an analysis of teleconnections between these predictors and the wintertime circulation over CA is conducted. The correlation analysis of predictors and large-scale circulation indices suggests a seasonal persistence of tropical circulation modes and a dynamical forcing of the westerly circulation by snow cover variations over Eurasia. An EOF analysis of pressure and humidity patterns allows separating the circulation variability over CA into westerly and tropical modes and confirms that the identified predictors affect the respective circulation characteristics. Based on the previously established weather type classification for CA, the predictors are investigated with regard to their effect on the regional circulation. The results suggest a modification of the Hadley cell due to ENSO variations, with enhanced moisture supply from the Arabian Gulf during El Nino. They further indicate an influence of Eurasian snow cover on the wintertime Arctic Oscillation (AO) and Northern Hemispheric Rossby wave tracks. Positive anomalies favor weather types associated with dry conditions, while negative anomalies promote the formation of a quasi-stationary trough over CA, which typically occurs during positive AO conditions

Variability of the cold season climate in Central Asia. Part I: weather types and their tropical and extratropical drivers

International audienceTo understand the atmospheric mechanisms resulting in a pronounced cold season climate variability in central Asia, an objective weather-type classification is conducted, utilizing a k-means-based clustering approach applied to 500-hPa geopotential height (GPH) fields. Eight weather types (WT) are identified and analyzed with regard to characteristic pressure patterns and moisture fluxes over Eurasia and specific near-surface climate conditions over central Asia. To identify remote drivers of the central Asian climate, WT frequencies are analyzed for their relationships with tropical and extratropical teleconnection modes. The results indicate an influence of Northern Hemispheric planetary wave tracks on westerly moisture fluxes with positive anomalies of precipitation associated with the formation of a Rossby trough over central Asia. Particularly the propagation of the east Atlantic-western Russia and the Scandinavian patterns is shown to modulate regional climate conditions. Variations of ENSO are shown to affect the frequency of particular WTs because of the formation of an anticyclonic anomaly over the Indian Ocean and an increase of tropical fluxes of moisture and heat into central Asia during El Nino events. Further a WT internal influence of ENSO is distinctly defined, with enhanced moisture supply during the ENSO warm phase. The analysis of climatic trends shows that 50% of observed temperature changes can be assigned to variations of the WT composition, indicating that most likely changing regional circulation characteristics account for the enhanced warming rates in central Asia. Trends of precipitation sums are likewise shown to be associated with changing WT frequencies, although the WT-precipitation relationships include large uncertainties

TREELINE - Longterm atmospheric and pedo-climatic observations along an upper treeline ecotone in the Himalayas, Nepal

This data set includes meteorological and pedo-climatic data obtained from a study area in central Himalayas, Nepal. The study area is located in the Rolwaling valley along the Rolwaling Himal in the Gaurishankar Conservation Area (GCA) in the Dolakha district. In this region the upper treeline ecotone is located in a subtropical high-altitude alpine forest zone with strong differences in land cover on opposing valley slopes. South-facing slopes are traditionally highly shaped by anthropogenic practice and dominant tree species forming the treeline are Juniperus spp. On north-facing slopes dense forests are consisting primarily of Betula utilis and Abies spectabilis with Rhododendron campanulatum, Sorbus microphylla, Acer caudatum and Prunus rufa communities. On the upper end of the main study site the forests are locked with a wide Rhododendron campanulatum krummholz belt which is followed by Rhododendron spp. dwarf shrubs and alpine tundra in higher areas. Within the east-west extending valley eight automatic weather stations (AWS) were installed since April 2013. These were mounted on different slopes and positions stretching from around 3700m up to over 5000m a.s.l. In 2m above surface (sensor type in brackets) incoming solar radiation [Wm-²] (ONS-S-LIB-M003), air temperature [°C] (ONS-S-THB-M002) and relative humidity [%] (ONS-S-THB-M002), wind speed [ms-1] (ONS-S-WSB-M003) and wind direction [°] (ONS-S-WDA-M003) as well as precipitation [mm] (ONS-S-RGB-M002) are measured every 3min and logged every 15min. Four AWS are positioned in the main study site in the valley representing two altitudinal transects along different slope expositions in north-western and north-eastern directions. Additionally 34 pedo-climatic Koubachi combined soil temperature [°C] and soil moisture [pF] loggers were installed from April 2013 until September 2015 in four different altitudinal belts along the transects in 10cm depth. These also follow the aforementioned pattern with different expositions and both parameters were logged in hourly intervals. Data quality: Winter precipitation is underestimated due to non-heated rain gauges. During the project start several vandalism incidents resulted in data loss and gaps in the time series. For hydrological modelling temperature and precipitation data sets are available in a gap filled version. Atmospheric sensors of AWS: 2m above surface: incoming solar radiation [Wm-²]; air temperature [°C]; relative humidity [%]; wind speed [ms-1]; wind direction [°]; precipitation [mm]; measuring interval: 3min; logging interval: 15min. Locations of automated weather stations (ONSET) Station; Longitude [°E]; Latitude [°N]; Altitude [m.a.s.l.]; Instal. Date; NW bottom; 86.3762; 27.9009;3718.9; 2013-04-18; NW top; 86.3742; 27.8967; 4035.9; 2013-04-15; NE bottom; 86.3791; 27.8986; 3734.2; 2013-04-18; NE bottom; 86.3791; 27.8986; 3734.2; 2013-04-18; NE top; 86.3759; 27.8934; 4158.3; 2013-10-17; Beding Gompa; 86.3755; 27.9050; 3886.0; 2013-04-19; Na; 86.4337; 27.8782; 4192.1; 2013-04-21; Dudgunda; 86.4604; 27.8756; 4532.2; 2016-09-29; Yalun; 86.4338; 27.8590; 5005.2; 2013-10-20; Pedo-climatic sensors of Koubachis: 10cm sensor soil depth; soil temperature [°C]; soil moisture [pF]; measuring and logging interval: 60min. Locations of pedo-climatic loggers (Koubachi AG) Belt / Logger groups; Altitudinal range [m a.s.l]; Altitudinal zone; Number of functional loggers (in 06/2015) NE-2 A; 3750 - 3900 m; closed forest; 4 (100%); NE-2 B; 3900 - 4000 m; uppermost closed forest; 2 (50%); NE-2 C; 4000 - 4100 m; krummholz belt; 4 (100%); NE-2 D; 4100 - 4250 m; dwarf scrub heath / alpine tundra; 3 (75%); NW-1 A; 3750 - 3800 m; closed forest; 3 (75%); NW-1 B; 3800 - 3900 m; uppermost closed forest; 3 (75%); NW-1 C; 3900 - 4050 m; krummholz belt; 3 (75%); NW-1 D; 4100 - 4250 m; dwarf scrub heath / alpine tundra; 3 (75%); NE-2 sp; 4000 - 4050 m; Abies spectabilis / Betula utilis individuals; 2 (100%); All data is stored as .csv files in UTF-8 encoding